Preprints
https://doi.org/10.5194/egusphere-2022-578
https://doi.org/10.5194/egusphere-2022-578
 
12 Jul 2022
12 Jul 2022
Status: this preprint is open for discussion.

A deep learning approach to increase the value of satellite data for PM2.5 monitoring in China

Bo Li1, Cheng Liu2,3,4,5, Qihou Hu3, Mingzhai Sun2, Chengxin Zhang2, Shulin Zhang2, Yizhi Zhu2, Ting Liu1, Yike Guo6, Gregory R. Carmichael7, and Meng Gao8,9 Bo Li et al.
  • 1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
  • 2Department of Precision Machinery and Precision Instrumentation, University of Scienceand8 Technology of China, Hefei 230027, China
  • 3Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
  • 4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
  • 5Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230027, China
  • 6Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
  • 7Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA52242, USA
  • 8Department of Geography, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR, China
  • 9John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA

Abstract. Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We use sensitivity analysis and visualization technology to open the neural network black box data model, and quantitatively discuss the potential impact of the input data on the target variables. This technique provides ground-level PM2.5 concentrations with high spatial resolution (0.01°) and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.

Bo Li et al.

Status: open (until 28 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Bo Li et al.

Data sets

POI the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=330

GDP the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=252

Population the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=251

MODIS land cover type Mark Friedl https://doi.org/10.5067/MODIS/MCD12C1.006

DEM the Resource and Environment Science Data Center https://www.resdc.cn/data.aspx?DATAID=123

MODIS aerosol optical depth Rob Levy and Christina Hsu https://doi.org/10.5067/MODIS/MOD04_3K.061

Himawari-8 satellite aerosol optical depth Yoshida, M. https://doi.org/10.2151/jmsj.2018-039

site pm2.5 CNEMC http://www.cnemc.cn/

weather fields the National Centers for Environment Prediction https://www.mmm.ucar.edu/weather-research-and-forecasting-model

road network openstreetmap https://download.geofabrik.de/asia/china.html

Bo Li et al.

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Short summary
Ambient particles have an important impact on human health, meteorology and climate change. By building a deep spatiotemporal neural network model we have overcome the long-standing limitations and get the full time and space coverage ground PM2.5 concentrations. We open the neural network black box data model by using sensitivity analysis and visualization techniques. This research will help improve health effects studies, climate effects of aerosols, and air quality prediction.